The problem of genome assembly is ultimately linked to the problem of the characterization of all repeat families in a genome as a repeat graph. The key reason the de Bruijn graph emerged as a popular short read assembly approach is because it offered an elegant representation of all repeats in a genome that reveals their mosaic structure. However, most algorithms for assembling long error-prone reads use an alternative overlap-layout-consensus (OLC) approach that does not provide a repeat characterization. We present the Flye algorithm for constructing the A-Bruijn (assembly) graph from long error-prone reads, that, in contrast to the k-mer-based de Bruijn graph, assembles genomes using an alignment-based A-Bruijn graph. In difference from existing assemblers, Flye does not attempt to construct accurate contigs (at least at the initial assembly stage) but instead simply generates arbitrary paths in the (unknown) assembly graph and further constructs an assembly graph from these paths. Counter-intuitively, this fast but seemingly reckless approach results in the same graph as the assembly graph constructed from accurate contigs. Flye constructs (overlapping) contigs with possible assembly errors at the initial stage, combines them into an accurate assembly graph, resolves repeats in the assembly graph using small variations between various repeat instances that were left unresolved during the initial assembly stage, constructs a new, less tangled assembly graph based on resolved repeats, and finally outputs accurate contigs as paths in this graph. We benchmark Flye against several state-of-the-art Single Molecule Sequencing assemblers and demonstrate that it generates better or comparable assemblies for all analyzed datasets..

The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.de Bruijn graph | genome assembly | single-molecule sequencing T he key challenge to the success of single-molecule sequencing (SMS) technologies lies in the development of algorithms for assembling genomes from long but inaccurate reads. The pioneer in long reads technologies, Pacific Biosciences, now produces accurate assemblies from long error-prone reads (1, 2). Goodwin et al. (3) and Loman et al. (4) demonstrated that high-quality assemblies can be obtained from even less-accurate Oxford Nanopore reads. Advances in assembly of long errorprone reads recently resulted in the accurate reconstructions of various genomes (5-10). However, as illustrated in Booher et al. (11), the problem of assembling long error-prone reads is far from being resolved even in the case of relatively small bacterial genomes.Previous studies of SMS assemblies were based on the overlaplayout-consensus (OLC) approach (12) or a similar string graph approach (13), which require an all-against-all comparison of reads (14) and remain computationally challenging (see refs. 15-17 for a discussion of the pros and cons of this approach). Moreover, there is an assumption that the de Bruijn graph approach, which has dominated genome assembly for the last decade, is inapplicable to long reads. This is a misunderstanding, because the de Bruijn graph approach, as well as its variation called the A-Bruijn graph approach, was developed to assemble rather long Sanger reads (18). There is also a misunderstanding that the de Bruijn graph approach can only assemble highly accurate reads and fails when assembling long error-prone reads. Although this is true for the original de Bruijn graph approach to assembly (15-17), the A-Bruijn graph approach was originally designed to assemble inaccurate reads as long as any similarities between reads can be reliably identified. Moreover, A-Bruijn graphs have proven to be useful even for assembling mass spectra, which represent highly inaccurate fingerprints of amino acid sequences of peptides (19,20). However, although A-Bruijn graphs have proven to be useful in assembling Sanger reads and mass spectra, the question of how to apply A-Bruijn graphs for assembling long error-prone reads remains open.de Bruijn graphs are a key algorithmic technique in genome assembly (15,(21)(22)(23)(24). In addition, de Bruijn graphs have been used for sequencing by hybridization (...

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by datadependent acquisition (DDA) experiments are required prior to DIA analysis, which is timeconsuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

BackgroundThe rapid accumulation of whole-genome data has renewed interest in the study of using gene-order data for phylogenetic analyses and ancestral reconstruction. Current software and web servers typically do not support duplication and loss events along with rearrangements.ResultsMLGO (Maximum Likelihood for Gene-Order Analysis) is a web tool for the reconstruction of phylogeny and/or ancestral genomes from gene-order data. MLGO is based on likelihood computation and shows advantages over existing methods in terms of accuracy, scalability and flexibility.ConclusionsTo the best of our knowledge, it is the first web tool for analysis of large-scale genomic changes including not only rearrangements but also gene insertions, deletions and duplications. The web tool is available from http://www.geneorder.org/server.php.

HighlightOf twenty-three TCP genes identified from the genome of Phalaenopsis orchid, PePCF10 and PeCIN8 play important roles in orchid ovule development by modulating cell division.